WTF??!!! POLLSTERS WITH THEIR “PREDICTIVE ANALYTICS” GOT THE ELECTION ALL WRONG – WHAT IT MIGHT MEAN FOR HR AND HR TECHNOLOGY.

As clearly evidenced by the Trump election and the fact that a vast majority (if not all!) of the pollsters who predicted a different outcome were wrong. With all their algorithms and underlying BIG DATA points – they still got the outcome wrong. The issue, in my humble opinion, is simple – nothing is easy or straightforward when trying to predict human behavior.

In my sphere of influence, that of the HRMS and HR’s use of technology, we seem to be (up to Nov. 8 at least) enamored with the concept of “predictive analytics.”

So, one issue that deserves some thought – does HR’s utilization of HRMS delivered predictive analytics to bolster and support actionable strategies regarding decisions affecting the workforce – globally or even individually – have a strong basis for validity? No less reliability?

Certainly, many HR technology software providers have, in the last few years created and delivered thoughtful and reasonable workforce metrics.

Those almost “out of the box” reports have added tremendous “Clout” to the role of HR in an organization. The software puts in the hands of HR Technology executives and HR C-level executives the opportunity to move from “Data Management to Information Craftsmanship” – providing future projections of workforce staffing levels and strong indications of the pipeline of talent, among other types of forecasts, in keeping with many well known lists of HR metrics.

Models and forecasts for staffing and performance outcomes based on incumbent performance have proven to have a basis of validity and reliability, as much research has already shown.

Executive and Management dependence on an effective HRMS delivering metrics and dashboards is certainly is needed and justified and would prove effective in a majority of cases calling for deep dive and a view of the underlying raw data that can enable cause and effect analysis.

More recently, a few of the top tier comprehensive HRMS providers and the “best of breed” niche functionality providers in talent management suites have begun to emphasize their concept and definition of “Predictive Analytics” which seemingly attempts to incorporate behavioral analysis added to the standby standard metrics related to HR management. However, the approach must meet the challenges of quantifying human behavior, and must incorporate additional aspects and applications of social media, beyond the input of baseline data elements.

I think the HRMS provider community will endeavor to do this, but there are some caveats.

Specifically, with regard to the behavior of people in any company’s workforce, as we have seen, human beings are under many influences – some stated and some left or kept private by any one individual. So predictions – for example – the “9 box model” of predicting which employees are prone to leave (especially those that are considered “key”) – a popular and often “out of the box” delivered metric by a good many leading HRMS vendors, must, or might, or should be viewed (and acted upon) with some skepticism. Is the report truly reliable and useful? Are we relying too much on statistics based on data, instead of listening to our “gut” and observing behavior about specific individuals?. It could be so.

Could taking action based on such predictive “models” end up overtaking other important inputs? Again, it could be so.

Larry Acton, in his “Under 30” blog (Forbes – 11/18/16) “Can you quantify your Human Resources Department?” points out that HR poses a unique problem in the field of business analytics because its bottom-line goals involve a degree of subjectivity and because not all employee actions and behaviors can be easily quantified and humans behave, well, like humans, even lying when asked their opinion (or, how they voted in exit polls – as we found out,) not to generalize – but still.

HR executives and managers must consider, and take advantage of all points of intersections between any employee and his/her manager. That would include such standard input resulting from frequent communications and exchanges. Exchanges begins with some formalized interactions – usually a performance review process. But by no means should that be the sole dialogue, and it would be a mistake to think that any exchange of future “engagement” on the part of the employee is fully candid or even truthful.

How HR Can Currently Be Quantified

Moving into 2017, HRMS providers will undoubtedly seek to integrate even more Organizational Behavior and Industrial Psychological aspects into their efforts in delivering more meaningful, and selective Predictive Analytics for which they have the underlying data.

Here are 3 areas of HR measurement that may become potential breakthrough metrics to be seen shortly – integrated within Talent Management and general HRMS delivered functionality:

Worker satisfaction. Currently, most businesses employ some system of employee feedback, both to gauge worker satisfaction for the purposes of retention and to learn ways they can improve the business from within. These surveys often attempt to force workers to quantify their subjective opinions, such as how satisfied they are with various qualities of their work environments using a five-point scale. When collected from many workers at once, it’s an effective way to get an “at-a-glance view” of your workers’ opinions. However, it is well known that a “satisfied” employee may not be performing at the level of a well “engaged” employee. Much has been written about “employee engagement” and it’s impact on the corporation’s performance. So asking an employee about their “satisfaction” can be misleading, especially if that employee feels satisfied and yet puts in an acceptable but minimal effort in doing his or her job and is not willing to any extra effort – as usually demonstrated by an “engaged” employee. The challenge? Will the workers be truthful when asked, and are they being asked the correct questions?

Productivity and performance. Most businesses also have some system of tracking productivity and performance. Sometimes, it’s a matter of measuring objective performance indicators, such as closed sales or how much time employees spend on various tasks in a time-tracking app. Other times, it’s an evaluation from a superior, using a similar point-based scale to the employee satisfaction survey. In this way, in theory, it’s possible to “ballpark” an employee’s contribution to a company’s value (compared to his/her salary and benefits). The challenge? Will performance evaluation and rewards that are based on time utilization be valid? Unless a time capture method is introduced for exempt level jobs and specific tasks, what stops a particular employee to alter or misrepresent the time he/she has spent doing a specific task to their own benefit? It is only human behavior and might even be done unconsciously?

Turnover and retention. Businesses also need to keep top talent around for as long as possible, and measuring turnover and retention rates is a good way to evaluate company performance. For example, a business may measure the percentage of employees who leave in a given year, and attempt to trace back any changes to meaningful changes they instated in HR policies or company direction. Still, when employees leave, most businesses conduct a qualitative “exit interview” that asks employees for their non-quantifiable opinions about their past work environment and how it could be improved. Again the question surfaces – will their comments really reflect how they feel? Millennials as a group have long been documented as being less loyal and more difficult to become fully “engaged” and less likely to stay beyond a few formative years in any one company. Even if that millennial employee is told that they are considered “key” or on the “fast track” to something more enriching it is subjective and maybe misleading to assume what is really on their minds.

So, there are obvious challenges when judging the accuracy of predictive analytics. Most are driven by the simple facts of human behavior.

For example, as Larry Acton describes, we should consider the multiple dimensions and thus multiple inputs to determine the attributes of a “good” worker:

Moods and atmosphere. Worker moods can have a powerful effect on the atmosphere of your work environment, and your overall productivity as a group. They’re somewhat contagious, and they change day to day, so it’s unlikely they’ll make a concrete or measurable appearance in one of your employee performance reports. For example, if one employee’s positivity consistently boosts the productivity of everyone around him/her, but his/her personal performance is below average, his/her productivity reports may not accurately reflect his/her full contributions to the company culture.

Social interaction. You may be able to track some employee interactions based on how they are using email, text and instant message apps. Such apps as “Facebook for Work” are now readily being adapted in many organizations. But how can you objectively capture a person’s contribution to the “social” element of working together? Teamwork and collaboration are major elements in a company’s overall productivity, but the rapport your workers have with each other may not appear in your employee surveys. For example, two employees in your marketing department may have had a “satisfactory” experience working with each other, but how well did they get to know each other? Do they feel more loyal to the company now that they feel closer? Will they be truthful about their feelings towards each other?

All this, on top of the challenge of having Teams that are totally Virtual, with team members located anywhere in the world and never interacting in person (in the same room). Additionally, by virtue of being global and virtual, teams are usually staffed with colleagues of many cultural differences – especially in the way they communicate and interact.

A leading expert in Virtual Team communications Ms. Yael Zofi (www.yaelzofi.com & founder of AIM-Strategies – told me that Virtual Teams “must be connected to each other in a way that encourages people to rise above their differences and connect at the human level to achieve results. Successful organizations embed connection and a global approach at every level. Our workdays are complex, and these connections enable us to communicate openly with diverse colleagues to keep projects moving and achieve competitive advantage. Ms Zofi went on to say that “as new generations step into the workforce their lifestyles will lend themselves to flexible work arrangements, and virtual work will become an accepted norm. Advances in technology will create more options to work in more convenient places. People will need to continue to create their own strategies to form an acceptable boundary between work and home.”

The challenge to valid Predictive Analytics gets even more difficult when looking at the future of staffing for the members of virtual teams, as the inputs are more difficult to obtain and without personal on-going face to face, person to person interaction, might even be suspect.

Intangible qualities. There are also intangible qualities that sometimes make people better for their jobs. For example, your salesperson may be inherently “likable,” which in turn may lead to more closed sales, but the level of influence here can go beyond these basic metrics. What about your accountant’s ability to answer questions about invoicing quickly, offline, and with fast precision? How can you effectively and quantitatively measure this effect?

These are also reasons why selecting a candidate should be about more than just the accomplishments they list on their resumes.

Data management and analytics are vital fields for businesses, but that doesn’t mean they are without challenges and limitations. As HRMS providers produce more sophisticated technology, it may become possible to quantify previously unquantifiable dimensions of human resources, but my guess is there will always be some degree of uncertainty and subjectivity with the “human” dimensions of worker productivity and satisfaction. Until then, gather what data you can, use it to make the most objective decisions possible, and don’t be afraid to evaluate your workers as people, rather than just data points.

As we have seen during the election of Mr Trump, and the predictive capabilities of the pollsters, human behavior is exactly that, human, and thus not infallible. In the workforce environment not all employee actions can be easily predicted, no less quantified.

The application of business analytics as an indicator of the thoughts and mindset of the workforce carries its own potential for surprises. As the people who make a good living providing predictions to the media, or (who are the media) and elsewhere have learned on Election day.

HR and HR technology colleagues, as we gain much needed and important metrics and other tools that claim to help us predict workforce behavior, we must keep in mind the cautionary tale of the pollsters performance in the Election of 2016.